Prevalence and influences of diabetes and prediabetes among adults living with HIV in Africa: a systematic review and meta‐analysis

Abstract Introduction In people living with human immunodeficiency virus (PLHIV), traditional cardiovascular risk factors, exposure to HIV per se and antiretroviral therapy (ART) are assumed to contribute to cardiometabolic diseases. Nevertheless, controversy exists on the relationship of HIV and ART with diabetes. To clarify the relationship between HIV and type 2 diabetes, this review determined, in PLHIV in Africa, diabetes and prediabetes prevalence, and the extent to which their relationship was modified by socio‐demographic characteristics, body mass index (BMI), diagnostic definitions used for diabetes and prediabetes, and HIV‐related characteristics, including CD4 count, and use and duration of ART. Methods For this systematic review and meta‐analysis (PROSPERO registration CRD42021231547), a comprehensive search of major databases (PubMed‐MEDLINE, Scopus, Web of Science, Google Scholar and WHO Global Health Library) was conducted. Original research articles published between 2000 and 2021 in English and French were included, irrespective of study design, data collection techniques and diagnostic definitions used. Observational studies comprising at least 30 PLHIV and reporting on diabetes and/or prediabetes prevalence in Africa were included. Study‐specific estimates were pooled using random effects models to generate the overall prevalence for each diagnostic definition. Data analyses used R statistical software and “meta” package. Results Of the 2614 records initially screened, 366 full‐text articles were assessed for eligibility and 61 were selected. In the systematic review, all studies were cross‐sectional by design and clinic‐based, except for five population‐based studies. Across studies included in the meta‐analysis, the proportion of men was 16–84%. Mean/median age was 30–62 years. Among 86,412 and 7976 participants, diabetes and prediabetes prevalence rates were 5.1% (95% CI: 4.3–5.9) and 15.1% (9.7–21.5). Self‐reported diabetes (3.5%) was lower than when combined with biochemical assessments (6.2%; 7.2%). Discussion While not statistically significant, diabetes and prediabetes were higher with greater BMI, in older participants, urban residents and more recent publications. Diabetes and prediabetes were not significantly different by HIV‐related factors, including CD4 count and ART. Conclusions Although HIV‐related factors did not modify prevalence, the diabetes burden in African PLHIV was considerable with suboptimal detection, and likely influenced by traditional risk factors. Furthermore, high prediabetes prevalence foreshadows substantial increases in future diabetes in African PLHIV.

maturing of the HIV epidemic on the continent with ageing populations has subsequently led to exposure to NCDs, and a parallel increase in cardiovascular and cardiometabolic diseases.
The aetiology of cardiometabolic diseases in PLHIV is multifactorial. Together with traditional cardiovascular risk factors, such as ageing, obesity, unhealthy lifestyles and so on, exposure to HIV per se and HAART are assumed to contribute to cardiometabolic diseases [1,2]. The use of HAART long-term has been linked to dysregulation of glucose metabolism and dyslipidaemia, chronic systemic inflammation, endothelial dysfunction and an increase in cardiovascular disease (CVD) risk [1,[3][4][5].
Nevertheless, controversy exists, and debate is ongoing on the relationship of HIV and HAART with type 2 diabetes mellitus (hereafter referred to as diabetes); both increased risk and no difference have been described in European populations in high-income countries [6]. In Africa, the global region with the greatest HIV burden (over 25 million individuals) [7], a meta-analysis of a few heterogeneous studies published between 2008 and 2016, and with moderate-tohigh risk of bias, revealed no significant association between prevalent diabetes and HIV or antiretroviral therapy (ART) [4]. In contrast, systematic reviews of studies prior to 2017 conducted in PLHIV globally have reported significant relationships between ART use and diabetes or prediabetes [5,8]. Nevertheless, these reviews have highlighted the need for further research to explore the interactions between prediabetes and/or diabetes with ART in PLHIV [5].
Diabetes in PLHIV in Africa is poorly understood with insufficient information on the epidemiology and influences of this complex condition. This is of concern because of the increasing diabetes burden in general populations in Africa attributable to traditional risk factors [9], and likely a similar pattern in PLHIV. Moreover, unlike HIV, diabetes is inadequately detected and poorly controlled in Africa leading to a rising burden linked to premature death [9,10]. Diabetes has the potential to threaten the advances in longevity achieved with the advent of ART in PLHIV in Africa [5]. Exploring and understanding the link between HIV and diabetes is important to maintain the advances made in the battle against HIV. Such information can inform strategies and interventions to effectively address comorbid diabetes in PLHIV [1,2,6].
This systematic review and meta-analysis aimed to determine the pooled prevalence of diabetes and its precursor state, prediabetes, among adult PLHIV in Africa. Additionally, the meta-analysis examined the magnitude of diabetes and prediabetes prevalence by socio-demographic characteristics (age, gender and urban/rural residence), body mass index (BMI), diagnostic definitions used for diabetes and prediabetes, and HIV-related characteristics (CD4 count, and use and duration of ART), among other predictive characteristics.

M E T H O D S
This systematic review, focusing on the prevalence of prediabetes and diabetes in PLHIV in Africa (including North Africa), was registered in the PROSPERO registry for systematic reviews (registration number CRD42021231547) [11].
The systematic review and meta-analyses were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA guidelines) [12].

Search strategy
A comprehensive electronic search was conducted across major databases, including PubMed-MEDLINE, Scopus, Web of Science, Google Scholar and WHO Global Health Library. This was supplemented with manual scanning of reference lists of relevant articles and reviews. The search terms comprised combinations of MeSH terms, CINAHL headings and free words relating to prevalence, diabetes, prediabetes and HIV/AIDS. The search terms for PubMed-MEDLINE are presented in Table S1 and were adapted accordingly for the other databases. The search was filtered for original research articles conducted in Africa and published from 01 January 2000 to 31 December 2021 in English and French languages.

Selection of eligible studies and diagnostic criteria
Observational studies (cross-sectional, case-control and cohort studies) comprising at least 30 people, that reported on the prevalence of diabetes and/or prediabetes among adult PLHIV in Africa, were included. Studies reporting outcomes in pregnant women, children or type 1 diabetes only were excluded.
For the overall estimate of diabetes prevalence, each study was included once only irrespective of the number of criteria used for diagnosis. A tiered approach was used to include a single prevalence estimate as follows: (1) OGTT; (2) FBG; and (3) RBG. For example, if a study reported diabetes prevalence using both OGTT and FBG, the OGTT-based diabetes prevalence was selected. Studies with self-report diabetes estimates or data extracted from clinic folders were included. However, studies that utilized HbA1c only for the diagnosis of diabetes were excluded from the overall diabetes prevalence estimate because HbA1c has not yet been recommended for diabetes diagnostic purposes in African populations.
Similarly, for the overall estimate of prediabetes prevalence, the tiered approach was as follows: (1) OGTT; (2) IGT; (3) IFG; and (4) RBG. Two pooled prevalence estimates for prediabetes, with and without studies that used HbA1c only, were calculated. The studies that utilized HbA1c only were excluded from the sub-group analyses.

Screening and data extraction
The studies were independently reviewed (KAN and NP) by title and abstract for eligibility, followed by an assessment of the relevant full texts. Disagreements were resolved by discussion and consensus or in consultation with a third investigator (APK). Relevant data for this review, extracted using a data extraction form designed for this review, included the following: (1)

Assessment of the methodological quality of included studies
The methodological quality of the included studies was evaluated using a checklist adapted from Hoy et al. [13] and used in previous systematic reviews [14]. The representativeness of the sample, the sampling technique, the response rate, the data collection method, the measurement tools, the case definitions and the statistical reporting were evaluated. Each of the nine questions were scored as low [1] or high (0) risk of bias. The total scores determined the risk of bias as follows: low: 7-9, moderate: 4-6 and high: 0-3.
The interrater disagreement was resolved by consensus or in consultation with a third investigator (APK). The precision (C) or margin of error was estimated for each included study, considering the sample size (SS) and the observed prevalence (p) of diabetes/prediabetes from the formula SS = Z2_p_(1p)/C2, where Z is the z-value fixed at 1.96 across studies (corresponding to the 95% confidence interval). The desirable margin of error was 5% (0.05) or lower.

Data synthesis and analyses
Data analyses were conducted using the R statistical software and the "meta" package. For each included study, the unadjusted prevalence of diabetes and prediabetes were estimated overall and across the major sub-groups of interest. The study-specific estimates were pooled using random effects models to generate the overall prevalence of diabetes and prediabetes for each diagnostic definition. The variance of the raw prevalence was stabilized using the Freeman-Tukey double arc-sine transformation before pooling the data to minimize the effect of extreme prevalence on the overall estimates. Data are presented as prevalence (%) and 95% confidence intervals (CI). A p-value <0.05 described statistically significant differences in findings within each diagnostic criterion overall, and by sub-group analyses. Heterogeneity among studies was assessed using I 2 , Cochran's Q and H statistics. I 2 values of <50% represented low heterogeneity and >75% described high heterogeneity. Potential sources of heterogeneity were explored by comparing the prevalence of diabetes and prediabetes between sub-groups of interest. These comparisons used the Q-test based on the Analysis of the Variance. Differences in major characteristics, such as study design, study populations, and diagnostic criteria and cut points for diabetes and prediabetes, were used to define sub-groups of interest, for example discrete categories (gender, setting, year of publication, diagnostic criteria and ART use) or by using median values of the summary estimates for continuous characteristics (age, BMI, sample size and ART duration).
The presence of publication bias was assessed using the funnel plots. This was supplemented by formal statistical assessments using the Egger test of bias [15]. A p-value <0.05 illustrated a significant asymmetry of the funnel plot and evidence of publication bias. The Duval and Tweedie trim-andfill was used to adjust estimates for the effects of publication bias.
Ethical approval was not required as this was secondary analyses of published data.

The review processes and data extraction
After duplicate removals from the 4083 records identified, titles and abstracts of 2614 records were screened, and 366 full-text articles were assessed for eligibility ( Figure 1). Of these, 61 fulfilled the eligibility criteria and were included in this review. One article [16] reported surveys at three time points which were counted separately, making a total of 63 studies included in the meta-analysis. All relevant HIV-related factors (HIV staging and viral load) and co-morbidities (hypertension, dyslipidaemia and coinfections) were not extracted as planned because of the lack of such data or an inadequate number of studies reporting the requisite data for meaningful analyses.

Methodological quality of the included studies
The risk of bias assessment for the included studies is summarized in Figure 2. Eight studies had a low risk of bias and 55 studies had a moderate risk of bias. Among the latter, 33 studies involved less than 500 participants, while 16 studies reported using some form of random selection approach to select participants.
All studies were cross-sectional and clinic-based, except five studies that were population-based (four from South Africa Most studies (n = 44) had less than 1000 participants and 11 studies had between 1000 and 2000 participants; the sample sizes ranged from 107 to 41,891 participants. The proportion of males in the studies ranged from 16% to 84%. The mean/median age ranged from 30 to 62 years. Most studies were conducted in ≥18-year-old adults but a few focused on older adults (≥30 years: n = 3 [24,56,62]; ≥40 years: n = 3 [29,67,68]; ≥ 50 years: n = 1 [64]).

Biochemical tests utilized in included studies
Among the 63 included studies, 35 defined diabetes using biochemical criteria and/or a self-reported diagnosis, 20 defined diabetes using biochemical criteria only and 11 studies described self-reported diabetes only (Table S2). The most common biochemical tests used were OGTT and FBG (Table  S2). Seven studies used HbA1c alone to diagnose diabetes and were excluded from the overall diabetes prevalence estimate [24,31,38,51,59,62,68] because HbA1c has been shown to underperform in African populations [69]. Estimates from 56 studies were pooled to determine the overall diabetes prevalence. However, five studies reported diabetes prevalence in sub-groups only and were counted as separate studies. These included four that determined diabetes prevalence separately in ART naïve and ART users [18,25,49,57], and a single study that described diabetes prevalence in ART naïve, first-line ART users and second-line ART users [18]. Thus, a total of 62 studies were pooled to derive the overall diabetes prevalence estimate (Table 2). Two overall pooled prediabetes prevalence estimates were calculated across studies that utilized HbA1c (n = 30) and those that did not (n = 24). The six studies that diagnosed prediabetes using HbA1c alone were excluded from the subgroup analyses [31,38,51,59,62,67] (Tables S2 and S3). Among the studies that described prediabetes, IGT was the most frequently reported form, followed by IFG.

Prevalence of diabetes
The diabetes prevalence rates by biochemical tests and/or self-reported diabetes are illustrated in the forest plots in Figure 3. Overall, 3559 of the 86,412 participants included in the overall pooled estimate had diabetes, corresponding to a prevalence of 5.1% (95% CI: 4.3-5.9). Self-reported diabetes prevalence per se, at 3.5% (2.2-5.1), was much lower than when combined with biochemical assessments (OGTT and/or self-report: 6 . Diabetes prevalence was also not significantly different by HIV-related factors of CD4 count, ART use or duration of ART use. There was also substantial heterogeneity for the diabetes prevalence by sub-group analyses; the I 2 was between 92% and 97%, and p-heterogeneity <0.001. For the overall diabetes prevalence, there was some evidence of publication bias overall (p = 0.002 for the Egger test). There was also evidence of bias among studies conducted in clinic-based settings (p = 0.003), urban areas (p = 0.033) and in participants younger than 40 years of age (p = 0.002). In trim and fill analyses however, imputed studies always had implausible effect estimates, with diabetes prevalence always lower than 1%, and null in about half of imputed studies (Figures S1-S4). This is unlikely and suggests that publication bias was a spurious finding ( Figure S5).

Prevalence of prediabetes
The prediabetes prevalence is presented in Table S4 and Figure 4. Prevalence was similarly high across studies that did not use HbA1c and those that did: 15.1% (95% CI: 9.7-21.5) versus 15.2% (10.8-20.1). There was no significant difference in prediabetes prevalence by sub-groups (Table S4) Considerable heterogeneity was also apparent across studies for prediabetes prevalence with I 2 between 95% and 99%, and p-heterogeneity <0.001. There was some evidence of publication bias when studies that used HbA1c alone were not accounted for (p = 0.015 for the Egger test), but not when these studies were included in the analysis (p = 0.197). Evidence of bias was also apparent across clinic-based studies (p = 0.008) and those in urban settings (p = 0.017). In trim and fill analyses, imputed studies systematically had very high effect estimates, with prediabetes prevalence of 50% or higher. This is implausible and suggests that findings of publication bias were spurious findings ( Figures S6-S8).

D I S C U S S I O N
This systematic review and meta-analysis conducted in adult PLHIV in Africa illustrates an established burden of diabetes, at 5.1%, and a high prediabetes prevalence of 15.2%. This diabetes prevalence accords with the 5.3% age-adjusted diabetes prevalence in the Africa region reported in the 2021 International Diabetes Federation (IDF) Atlas [70]. The ageadjusted IGT and IFG prevalence rates in the Africa region were 12.6% and 8.0%, respectively. The comparative prevalence rates of diabetes and prediabetes in PLHIV compared with general populations likely suggest similar influences in their development of dysglycaemia.         not computable.  The prevalence of self-reported diabetes only (3.5%), which reflects diabetes awareness or detection rather than true prevalence, was lower than combined self-report with biochemically assessed diabetes (OGTT: 6.2%; FBG: 7.2%). Further, although not statistically significant, general trends in sub-group analyses conformed with traditional diabetes risk factors and were in the expected direction; prevalence rates were higher with older age, greater BMI and in urban residents. Notably, a rising prevalence of diabetes and prediabetes over time was suggested by higher rates in recent versus earlier publications. There were no clear trends for diabetes and prediabetes prevalence by HIV-related factors.
The lower prevalence of known or self-reported diabetes compared with diabetes prevalence identified on combined biochemical analyses with self-report suggests that a substantial proportion of PLHIV with co-morbid diabetes were undi-agnosed for the latter condition. This is likely similar to general populations in Africa where a substantial proportion of diabetes is undiagnosed [9,10]. However, unlike general populations, these PLHIV are in regular contact with health services and would be expected to have all co-morbidities, including diabetes identified. Unfortunately, in practice, ART is generally provided by international donors in Africa with little funding or care provided for NCDs [71]. Consequently, there are disparities in management with the free treatment provided for HIV but a minimal focus on diabetes and other CVDs, such as hypertension and dyslipidaemia. This is a missed opportunity to holistically manage the rise in NCD comorbidities in PLHIV in Africa. Policymakers should be alerted to the tangible shift in approach that is urgently required for the care of this vulnerable population. There needs to be a swing from a focus on HIV itself to a more comprehensive approach that encompasses the care of neglected NCD co-morbidities. This is important if the momentum gained in increasing life expectancy in PLHIV in Africa is to be maintained. Screening for diabetes should be included in routine assessments of PLHIV in Africa, which is currently not standard practice [8].
The urgent need for this shift in approach for the care of diabetes and other NCD co-morbidities in African PLHIV is underscored by the high burden of prediabetes demonstrated in this review; almost one in six people were affected. Generally, it is predicted that 5-10% of individuals with prediabetes will progress to diabetes annually [72]. This likely foretells of a substantial increase in diabetes prevalence in this vulnerable population in future. The effectiveness of HAART with increased longevity and subsequent ageing, and the uptake of unhealthy lifestyle behaviours will likely translate to the high prediabetes burden progressing to diabetes. A recent systematic review demonstrated that, similar to general populations, traditional risk factors, such as older age, diabetes family history, overweight/obesity and so on, were among the main contributors to the development of dysglycaemia in PLHIV globally, including in Africa [5]. The higher prevalence of diabetes and prediabetes with older age and higher BMI in the current review likely corroborates the influence of traditional risk factors in the development of dysglycaemia in African PLHIV. Therefore, the large burden of prediabetes in African PLHIV with the potential for conversion to diabetes in the future possibly mirrors the diabetes trends predicted for general populations in Africa.
Reinforcing the future expansion of diabetes in PLHIV in Africa, although not significant, was the higher prevalence of diabetes and prediabetes illustrated in recent years. This increasing pattern is likely a reflection of the diabetes trend predicted in general populations in Africa. The 4.7% diabetes prevalence estimated in Africa in 2019 is expected to rise to 5.2% by 2045 with a more than doubling of the absolute numbers [10]. The current literature and this review likely underline a shift in the disease burden from communicable diseases to NCDs in Africa with diabetes a significant disease entity in the region [9,73], even in PLHIV.
Similar to the findings of a systematic review conducted a few years ago but using different eligibility criteria for included studies [4], the current review found no statistically significant difference in diabetes prevalence by ART status. Two additional systematic reviews, one conducted in Sub-Saharan Africa [6] and the other in a few longitudinal studies in PLHIV globally [8], reported no association between ART use and FBG. Nevertheless, the uncertainty of the evidence is highlighted by the overall findings in the review by Nduka and colleagues, which included mainly cross-sectional studies. They reported an association between ART use and diabetes, diagnosed on mean FBG levels [8]. Moreover, a systematic review by Nansseu and colleagues of longitudinal studies conducted in PLHIV globally reported an association between a cumulative exposure to some ART drugs and incident diabetes and prediabetes, but this finding was not consistent across studies [5]. Despite their differences in the eligibility criteria for included studies, these systematic reviews underscore the absence of clear irrefutable evidence linking the development of diabetes with ART [74].
Over the last few years, there has been a change to the use of newer ART drugs with fewer metabolic effects [5]. Studies conducted in populations using newer drugs would have been unlikely to be included in the reviews of studies published prior to 2017. Further research detailing the newer ART drugs used in recent studies and their specific contributions to the development of diabetes, if any, is required. This includes dolutegravir, an integrase inhibitor, which has been found to be more effective and better tolerated than older ART medications, leading to its recommended use as a preferred first-and second-line ART by the World Health Organization [75]. Recent evidence from Africa describes greater odds of hyperglycaemia in PLHIV treated with dolutegravir compared with other ART regimens even after adjusting for potential confounders of age, BMI and co-morbid hypertension [75]. If a wider body of research confirms these findings, systematic screening for diabetes and prediabetes prior to the use of dolutegravir may need to be incorporated into HIV treatment guidelines [75].

Strengths and limitations
The strengths of this review include the following: (1) using a review protocol with a comprehensive and systematic search strategy examining five separate databases and the reference lists of eligible studies; (2) evaluating a large number of participants from different studies; and (3) using the Freeman-Tukey double arc-sine transformation which stabilized the variance of primary studies before combining the data; this limited the effect on the pooled estimates of studies with small or large prevalence rates. The limitations of this review include the following: (1) the restriction to English and French languages may have excluded eligible studies in other languages and introduced a language bias; (2) the inability to examine, because of insufficient data, the associations by ART drug category, which may have been clinically relevant; (3) the inability to describe, because of insufficient data, the associations by adiposity category, which may have underscored the importance of the relation of traditional risk factors with diabetes; (4) the inclusion of only cross-sectional studies precluded any causal inferences; (5) few (six) eligible studies had a low risk of bias; (6) the substantial heterogeneity among included studies; and (7) the inability to explore the association with a family history of diabetes, which is a key risk factor for diabetes; this was because of insufficient data.

C O N C L U S I O N S
As the diabetes epidemic worsens in Africa, adult PLHIV are affected as severely, and by similar socio-demographic and anthropometric factors, as Africans without HIV. Furthermore, the high prevalence of prediabetes portends a likely increase in future diabetes. Policymakers in African countries must be alerted to the need to integrate cost-effective and efficient screening, prevention and treatment of diabetes with HIV care; this will maintain the momentum and secure the advances made in optimizing HIV management. Otherwise, the future will witness a substantial proportion of PLHIV in Africa succumbing to premature diabetes and CVD-related morbidity and mortality. Evidence-based research is needed to provide guidance on the best strategies and approaches for the integration of diabetes and CVD prevention and care with HIV management. This review, comprising cross-sectional studies, highlights the lack of associations between diabetes and HIV-related factors of CD4 count, ART use and duration of ART use. Longitudinal studies are, therefore, needed to clearly elucidate the influences, both traditional and HIV related, on the development of diabetes in African PLHIV.

C O M P E T I N G I N T E R E S T S
None to declare for all co-authors.

D ATA A C C E S S , R E S P O N S I B I L I T Y A N D A N A LY S I S
KAN and APK had full access to all the data in the study and take responsibility for the integrity of the data and accuracy of the data analysis; both are guarantors.

D ATA AVA I L A B I L I T Y S TAT E M E N T
The study is based on aggregation of publicly available data from primary studies. As such, there are no data to be shared.

S U P P O R T I N G I N F O R M AT I O N
Additional information may be found under the Supporting Information tab for this article: Figure S1: Forest plot showing the overall pooled prevalence of diabetes in people living with HIV, from the trim and fill analyses. Figure S2: Forest plot showing the pooled prevalence of diabetes in people living with HIV in clinic-based studies, from the trim and fill analyses. Figure S3: Forest plot showing the pooled prevalence of diabetes in people living with HIV in urban settings, from the trim and fill analyses. Figure S4: Forest plot showing the pooled prevalence of diabetes in people younger than 40 years old living with HIV, from the trim and fill analyses . Figure S5: Funnel plots for studies that reported prevalence of diabetes in people living with HIV (A) overall and (B) in clinic-based settings from the trim and fill analyses. Black dots identify the actual studies while clear dots identify imputed studies. Figure S6: Forest plot showing the pooled prevalence of prediabetes in people living with HIV, from the trim and fill analyses. Figure S7: Forest plot showing the pooled prevalence of prediabetes in people living with HIV in studies in clinical settings, from the trim and fill analyses. Figure S8: Forest plot showing the pooled prevalence of prediabetes in people living with HIV in studies in urban areas, from the trim and fill analyses.